Recombinant human endostatin (rh-endostatin) is a novel antiangiogenesis drug developed in China. Previous experiments have shown that rh-endostatin can inhibit the proliferation and migration of endothelial cells and some types of tumor cells. In this study, we evaluated the efficacy and safety profiles of combination therapy of rh-endostatin and neoadjuvant chemotherapy for breast cancer patients in a prospective, randomized, controlled, phase II trial. Sixty-eight patients with core-biopsy confirmed breast cancer were allocated randomly to two groups to receive 3 cycles of intravenous administration of either neoadjuvant DE (docetaxel: 75 mg/m², d1, epirubicin: 75 mg/m², d1, every 3 weeks), or neoadjuvant DE combined with rh-endostatin (7.5 mg/m², d1-d14, every 3 weeks). The primary end point was clinical response based upon Response Evaluation Criteria in Solid Tumors, and the secondary end point was safety and quality of life. All patients were assessable for toxicity and 64 (94.2%) were assessable for efficacy evaluation. The objective response rate was 67.7% for chemotherapy (n = 31) and 90.9% for rh-endostatin plus chemotherapy (n = 33) (P = 0.021). A retrospective subset analysis revealed that rh-endostatin was more effective in premenopausal patients and patients with ECOG score of zero (P = 0.002 and P = 0.049, respectively). Five patients in the rh-endostatin plus chemotherapy arm achieved pathologic complete response compared with 2 in the chemotherapy arm (P = 0.428). No significant difference was identified in quality of life score and side effects (P > 0.05). The combination of rh-endostatin with chemotherapy produced a higher tumor response rate without increasing toxicity in breast cancer patients. ClinicalTrials.gov Identifier, NCT00604435

Background: The aim of this study was to compare intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) parameters such as standard apparent diffusion coefficient (ADC_standard), pure diffusion coefficient (D_slow), pseudodiffusion coefficient (D_fast) and perfusion fraction (ƒ) for differentiating pancreatic ductal adenocarcinoma (PDAC) with different pathological grades. Methods: Institutional Review Board of our hospital approved this study protocol. Subjects comprised 38 PDACs confirmed by pathology. Pancreatic multiple b values DWI with 15 b values of 0, 10, 20, 40, 60, 80, 100, 150, 200, 400, 800, 1,000, 1200, 1,500, and 2,000 s/mm² was performed using GE Discovery MR750 3.0T scanner. ADC_standard, D_slow, D_fast and ƒ values of all PDACs were calculated using mono- and bi-exponential models. Parameters of well/moderately differentiated and poorly differentiated PDAC were compared using Independent Sample t-test. P values <0.05 were considered significant. Results: Mean D_slow value of well/moderately differentiated PDAC was significantly lower than that of poorly differentiated PDAC (0.540×10⁻³ vs. 0.676×10⁻³ mm²/s, P<0.001). Mean ƒ value of well/moderately differentiated PDAC was significantly higher than that of poorly differentiated PDAC (60.3% vs. 38.4%, P0.865). When the D_slow value was less than or equal to 0.599×10⁻³ mm²/s, the sensitivity and specificity were 100% and 84.6% respectively. When ƒ value was greater than 49.6%, the sensitivity and specificity were 92.0% and 84.6% respectively. Conclusions: D_slow and ƒ derived from IVIM-DWI model can be used to distinguish well/moderately differentiated PDAC from poorly differentiated PDAC. And to serve this purpose, D_slow and ƒ have high diagnostic performance. IVIM-DWI is a promising and non-invasive tool for predicting pathological grade of PDAC.

Radiomic features extracted from segmented tumor regions have shown great power in gene mutation prediction, while deep learning–based (DL-based) segmentation helps to address the inherent limitations of manual segmentation. We therefore investigated whether deep learning–based segmentation is feasible in predicting KRAS/NRAS/BRAF mutations of rectal cancer using MR-based radiomics. In this study, we proposed DL-based segmentation models with 3D V-net architecture. One hundred and eight patients’ images (T2WI and DWI) were collected for training, and another 94 patients’ images were collected for validation. We evaluated the DL-based segmentation manner and compared it with the manual-based segmentation manner through comparing the gene prediction performance of six radiomics-based models on the test set. The performance of the DL-based segmentation was evaluated by Dice coefficients, which are 0.878 ± 0.214 and 0.955 ± 0.055 for T2WI and DWI, respectively. The performance of the radiomics-based model in gene prediction based on DL-segmented VOI was evaluated by AUCs (0.714 for T2WI, 0.816 for DWI, and 0.887 for T2WI+DWI), which were comparable to that of corresponding manual-based VOI (0.637 for T2WI, P=0.188; 0.872 for DWI, P=0.181; and 0.906 for T2WI+DWI, P=0.676). The results showed that 3D V-Net architecture could conduct reliable rectal cancer segmentation on T2WI and DWI images. All-relevant radiomics-based models presented similar performances in KRAS/NRAS/BRAF prediction between the two segmentation manners.